If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. Shopping can be tough when it comes to the kids in your life; sometimes it seems like all they need is a mobile device to play on, and they're all set. If they're already on a phone or tablet, though, why not buy them a toy that uses that screen time to good effect? One trend in toys today is to set children up on the STEM (science, technology, engineering, and math) path early with toys or games that encourage kids to learn and explore the fundamentals of computer programming while having fun at the same time.

Tech columnist Marc Saltzman gives tech ideas to keep you entertained during your "staycation" if you're home for the holidays. Don't be jealous if your snooty friends won't stop talking about their upcoming holiday vacation. Let them deal with crowded airports, sunburns, and watered-down booze at all-inclusive resorts. Instead, having a "staycation" has never been more fun – especially if you consider all the awesome home tech you can pick up for you and the family. From big-screen televisions and smart speakers that play millions of songs (simply by asking for it) to deals on video game consoles and curling up with a great e-book on a tablet, there's never been a better time to be home for the holidays.

Stuck on what to get your loved ones for the holidays? As a tech reviewer, I get to play with all the best new gadgets before they hit store shelves, but I don't get to keep them. That means this time every year, I'm leaning over my laptop -- fingers twitching and crediting cards itching -- to buy a few of my favorites for myself or people I really, really like. Here's what I shelled out my very own money to buy this year. This is the year of voice-activated smart displays.

Artificial intelligence influencers are driving conversations about AI news and trends across social media and beyond. They advise on company boards, build startups and are moulding an industry that is key in today's tech world, with implications going far beyond. Nearly 70 years on since Alan Turing posed the question: "Can machines think?", artificial intelligence is finally beginning to have a significant impact on the global economy. Proponents of AI believe that it has the potential to transform the world as we know it, with Google's chief executive officer Sundar Pichai describing AI as "more profound than electricity or fire" at an American television network MSNBC event in San Francisco. But AI is still in its infancy, and needs the nurturing hands of its influencers to help it grow, in spite of the research and development effort that has been put into it over the years.

For more than a decade, computer graphics (CG) researchers and visual effects experts have been fascinated with bringing photorealistic digital actors to the screen. Crossing the well-known "uncanny valley" in CG humans has been one of the most difficult and crucial challenges, due to hypersensitivity to synthetic humans lacking even the slightest and most subtle features of genuine human faces. Given sufficient resources and time, photorealistic renderings of digital characters have been achieved in recent years. Some of the most memorable cases are seen in blockbuster movies, such as The Curious Case of Benjamin Button, Furious 7, and Rogue One: A Star Wars Story, in which large teams of highly skilled digital artists use cutting-edge digitization technologies. Despite the progress of 3D-scanning solutions, facial animation systems, and advanced rendering techniques, weeks of manual work are still needed to produce even just a few seconds of animation.

Recent offline performance capture techniques approach the hard monocular reconstruction problem by fitting a blendshape or a multilinear face model to the input video sequence. Even geometric fine-scale surface detail is extracted via inverse shading-based surface refinement. Shi et al.16 achieve impressive results based on global energy optimization of a set of selected keyframes. Our model-based bundling formulation to recover actor identities is similar to their approach; however, we use robust and dense global photometric alignment, which we enforce with an efficient data-parallel optimization strategy on the Graphics Processing Unit (GPU).

Can you tell who is real and who is not? Artificial Intelligence is now able to create lifelike human faces from scratch. Researchers at NVIDIA have been working on creating realistic looking human faces from only a few source photos for years. For many people it's difficult to tell the difference between one of the faces generated below and an actual human face, can you spot which is which? The source image - the top row - are the only legitimate photographs of real people, the rest have been computer generated.

In this paper, we introduce a new task - interactive image editing via conversational language, where users can guide an agent to edit images via multi-turn dialogue in natural language. In each dialogue turn, the agent takes a source image and a natural language description from the user as the input, and generates a target image following the textual description. Two new datasets are created for this task,Zap-Seq and DeepFashion-Seq, collected via crowdsourcing. For this task, we propose a new Sequential Attention Genrative Adversarial Network (SeqAttnGAN) framework, which applies a neural state tracker to encode both source image and textual descriptions, and generates high quality images in each dialogue turn. To achieve better region specific text-to-image generation, we also introducean attention mechanism into the model. Experiments on the two datasets, including quantitative evaluation and user study, show that our model outperforms state-of-the-art ap-proaches in both image quality and text-to-image consistency.

Research on time-series similarity measures has emphasized the need for elastic methods which align the indices of pairs of time series and a plethora of non-parametric have been proposed for the task. On the other hand, deep learning approaches are dominant in closely related domains, such as learning image and text sentence similarity. In this paper, we propose \textit{NeuralWarp}, a novel measure that models the alignment of time-series indices in a deep representation space, by modeling a warping function as an upper level neural network between deeply-encoded time series values. Experimental results demonstrate that \textit{NeuralWarp} outperforms both non-parametric and un-warped deep models on a range of diverse real-life datasets.

On the surface, The Godfather, The Sixth Sense and Little Miss Sunshine appear to have little in common. But even though these films belong to different genres and have very different plots, technically they have the same "emotional arc" - a journey of highs and lows. Using artificial intelligence, we analysed more than 6,000 scripts from the past 80 years and discovered all films fall within six emotional arcs. These include the emotional rise of "rags to riches" films such as The Shawshank Redemption and the rise and fall of "man in a hole" films such as Who Framed Roger Rabbit. But which of these are the most successful, critically and commercially? Tragedies, which depict a continuing emotional fall, appear to receive the highest number of Oscar nominations per film.